Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials

This article explores the integration of artificial intelligence (AI) and advanced digital technologies into laser processing, highlighting their potential to enhance precision, efficiency, and process control. The study examines the application of digital twins and machine learning (ML) for optimizing laser machining, reducing defects, and improving the analysis of laser–material interactions. Emphasis is placed on AI’s role in additive manufacturing and microprocessing, particularly in real-time monitoring, defect prediction, and parameter optimization. Additionally, the article addresses emerging challenges, such as the adaptation of AI models to complex material behaviors and the integration of intelligent systems into existing manufacturing environments. The role of advanced optical technologies, such as free-form optics and diffractive optical elements, is discussed in relation to enhancing laser system adaptability and performance. The article concludes with a discussion on future trends, emphasizing the need for interdisciplinary collaboration to overcome technical and economic complexities while leveraging AI to meet the growing demand for precision and customization in industrial manufacturing.

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Publication Details

Journal
Metals
Published
2024-12-20
DOI
https://doi.org/10.3390/met14121458
Citations
18
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
3.83
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article

Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials

Serguei P. Murzin
18 citations
Metals
Additive Manufacturing Materials and Processes
3.83
article

Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials

Serguei P. Murzin
article en
18 citations

Abstract

This article explores the integration of artificial intelligence (AI) and advanced digital technologies into laser processing, highlighting their potential to enhance precision, efficiency, and process control. The study examines the application of digital twins and machine learning (ML) for optimizing laser machining, reducing defects, and improving the analysis of laser–material interactions. Emphasis is placed on AI’s role in additive manufacturing and microprocessing, particularly in real-time monitoring, defect prediction, and parameter optimization. Additionally, the article addresses emerging challenges, such as the adaptation of AI models to complex material behaviors and the integration of intelligent systems into existing manufacturing environments. The role of advanced optical technologies, such as free-form optics and diffractive optical elements, is discussed in relation to enhancing laser system adaptability and performance. The article concludes with a discussion on future trends, emphasizing the need for interdisciplinary collaboration to overcome technical and economic complexities while leveraging AI to meet the growing demand for precision and customization in industrial manufacturing.

MetalsVol. 14(12)
TU Wien (AT), Samara National Research University (RU)
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Additive Manufacturing Materials and Processes
3.83
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